In this notebook, some template code has already been provided for you, and you will need to implement additional functionality to successfully complete this project. You will not need to modify the included code beyond what is requested. Sections that begin with '(IMPLEMENTATION)' in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section, and the specifics of the implementation are marked in the code block with a 'TODO' statement. Please be sure to read the instructions carefully!
Note: Once you have completed all the code implementations, you need to finalize your work by exporting the Jupyter Notebook as an HTML document. Before exporting the notebook to HTML, all the code cells need to have been run so that reviewers can see the final implementation and output. You can then export the notebook by using the menu above and navigating to File -> Download as -> HTML (.html). Include the finished document along with this notebook as your submission.
In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a 'Question X' header. Carefully read each question and provide thorough answers in the following text boxes that begin with 'Answer:'. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.
Note: Code and Markdown cells can be executed using the Shift + Enter keyboard shortcut. Markdown cells can be edited by double-clicking the cell to enter edit mode.
The rubric contains optional "Stand Out Suggestions" for enhancing the project beyond the minimum requirements. If you decide to pursue the "Stand Out Suggestions", you should include the code in this Jupyter notebook.
Photo sharing and photo storage services like to have location data for each photo that is uploaded. With the location data, these services can build advanced features, such as automatic suggestion of relevant tags or automatic photo organization, which help provide a compelling user experience. Although a photo's location can often be obtained by looking at the photo's metadata, many photos uploaded to these services will not have location metadata available. This can happen when, for example, the camera capturing the picture does not have GPS or if a photo's metadata is scrubbed due to privacy concerns.
If no location metadata for an image is available, one way to infer the location is to detect and classify a discernible landmark in the image. Given the large number of landmarks across the world and the immense volume of images that are uploaded to photo sharing services, using human judgement to classify these landmarks would not be feasible.
In this notebook, you will take the first steps towards addressing this problem by building models to automatically predict the location of the image based on any landmarks depicted in the image. At the end of this project, your code will accept any user-supplied image as input and suggest the top k most relevant landmarks from 50 possible landmarks from across the world. The image below displays a potential sample output of your finished project.

We break the notebook into separate steps. Feel free to use the links below to navigate the notebook.
Note: if you are using the Udacity workspace, YOU CAN SKIP THIS STEP. The dataset can be found in the /data folder and all required Python modules have been installed in the workspace.
Download the landmark dataset.
Unzip the folder and place it in this project's home directory, at the location /landmark_images.
Install the following Python modules:
In this step, you will create a CNN that classifies landmarks. You must create your CNN from scratch (so, you can't use transfer learning yet!), and you must attain a test accuracy of at least 20%.
Although 20% may seem low at first glance, it seems more reasonable after realizing how difficult of a problem this is. Many times, an image that is taken at a landmark captures a fairly mundane image of an animal or plant, like in the following picture.
Just by looking at that image alone, would you have been able to guess that it was taken at the Haleakalā National Park in Hawaii?
An accuracy of 20% is significantly better than random guessing, which would provide an accuracy of just 2%. In Step 2 of this notebook, you will have the opportunity to greatly improve accuracy by using transfer learning to create a CNN.
Remember that practice is far ahead of theory in deep learning. Experiment with many different architectures, and trust your intuition. And, of course, have fun!
Use the code cell below to create three separate data loaders: one for training data, one for validation data, and one for test data. Randomly split the images located at landmark_images/train to create the train and validation data loaders, and use the images located at landmark_images/test to create the test data loader.
All three of your data loaders should be accessible via a dictionary named loaders_scratch. Your train data loader should be at loaders_scratch['train'], your validation data loader should be at loaders_scratch['valid'], and your test data loader should be at loaders_scratch['test'].
You may find this documentation on custom datasets to be a useful resource. If you are interested in augmenting your training and/or validation data, check out the wide variety of transforms!
### TODO: Write data loaders for training, validation, and test sets
## Specify appropriate transforms, and batch_sizes
import torch
from torch.utils.data.dataloader import DataLoader
from torchvision.datasets import ImageFolder
from torchvision import transforms
import numpy as np
from torch.utils.data.sampler import SubsetRandomSampler
data_transform_train = transforms.Compose([
transforms.Resize((256,256)),
transforms.RandomHorizontalFlip(),
transforms.RandomRotation(20),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
data_transform_val_test = transforms.Compose([
transforms.Resize((256,256)),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
batch_size = 40
num_workers = 4
data_root = "./landmark_images"
train_data= ImageFolder(f'{data_root}/train',transform=data_transform_train)
test_data = ImageFolder(f'{data_root}/test',transform=data_transform_val_test)
# obtain training indices that will be used for validation
valid_size = 0.2
num_train = len(train_data)
indices = list(range(num_train))
np.random.shuffle(indices)
split = int(np.floor(valid_size * num_train))
train_idx, valid_idx = indices[split:], indices[:split]
train_sampler = SubsetRandomSampler(train_idx)
valid_sampler = SubsetRandomSampler(valid_idx)
loaders_scratch = {'train': DataLoader(train_data,batch_size=batch_size,num_workers=num_workers, sampler=train_sampler),
'valid': DataLoader(train_data,batch_size=batch_size,num_workers=num_workers, sampler=valid_sampler),
'test': DataLoader(test_data,batch_size=batch_size,num_workers=num_workers, shuffle=True)
}
Question 1: Describe your chosen procedure for preprocessing the data.
Answer:
Use the code cell below to retrieve a batch of images from your train data loader, display at least 5 images simultaneously, and label each displayed image with its class name (e.g., "Golden Gate Bridge").
Visualizing the output of your data loader is a great way to ensure that your data loading and preprocessing are working as expected.
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
## TODO: visualize a batch of the train data loader
## the class names can be accessed at the `classes` attribute
## of your dataset object (e.g., `train_dataset.classes`)
dataiter = iter(loaders_scratch['test'])
images, labels = next(dataiter)
images = images.numpy()
print(images.shape)
# plot the images in the batch, along with the corresponding labels
fig = plt.figure(figsize=(25, 4))
for idx in np.arange(batch_size):
ax = fig.add_subplot(2, int(40/2), idx+1, xticks=[], yticks=[])
ax.imshow(np.squeeze(images[idx].transpose((1,2,0))), cmap='gray')
# print out the correct label for each image
# .item() gets the value contained in a Tensor
ax.set_title(str(labels[idx].item()))
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
(40, 3, 256, 256)
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
# useful variable that tells us whether we should use the GPU
use_cuda = torch.cuda.is_available()
print(use_cuda)
True
Use the next code cell to specify a loss function and optimizer. Save the chosen loss function as criterion_scratch, and fill in the function get_optimizer_scratch below.
import torch.nn as nn
## TODO: select loss function
criterion_scratch = nn.CrossEntropyLoss()
def get_optimizer_scratch(model):
## TODO: select and return an optimizer
return torch.optim.SGD(model.parameters(), lr=0.02, momentum=0.9)
Create a CNN to classify images of landmarks. Use the template in the code cell below.
import torch.nn as nn
import torch.nn.functional as F
# define the CNN architecture
# Inspired by Cifar Excercise notebook
class Net(nn.Module):
## TODO: choose an architecture, and complete the class
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 16, 3, padding=1)
self.conv2 = nn.Conv2d(16, 32, 3, padding=1)
self.conv3 = nn.Conv2d(32, 64, 3, padding=1)
self.conv4 = nn.Conv2d(64, 128, 3, padding=1)
self.conv1_bn = nn.BatchNorm2d(16)
self.conv2_bn = nn.BatchNorm2d(32)
self.conv3_bn = nn.BatchNorm2d(64)
self.conv4_bn = nn.BatchNorm2d(128)
self.pool = nn.MaxPool2d(2, 2)
self.fc1 = nn.Linear(128 * 16 * 16, 512)
self.fc1_bn = nn.BatchNorm1d(512)
self.fc2 = nn.Linear(512, 50)
self.dropout = nn.Dropout(0.25)
def forward(self, x):
x = self.pool(F.relu(self.conv1_bn(self.conv1(x))))
x = self.pool(F.relu(self.conv2_bn(self.conv2(x))))
x = self.pool(F.relu(self.conv3_bn(self.conv3(x))))
x = self.pool(F.relu(self.conv4_bn(self.conv4(x))))
# flatten image input
x = x.view(-1, 128 * 16 * 16)
x = self.dropout(x)
x = F.relu(self.fc1_bn(self.fc1(x)))
x = self.dropout(x)
x = self.fc2(x)
return x
#-#-# Do NOT modify the code below this line. #-#-#
# instantiate the CNN
model_scratch = Net()
# move tensors to GPU if CUDA is available
if use_cuda:
model_scratch.cuda()
Question 2: Outline the steps you took to get to your final CNN architecture and your reasoning at each step.
Answer:
I started with the definition of the Net from previous excercise. Then I edited the values so the Net can take bigger images. i also Used Batch Normalization, per feedback from first assignment.
Implement your training algorithm in the code cell below. Save the final model parameters at the filepath stored in the variable save_path.
from pathlib import Path
#Inspired by Cifar Excercise
def train(n_epochs, loaders, model, optimizer, criterion, use_cuda, save_path):
"""returns trained model"""
# initialize tracker for minimum validation loss
valid_loss_min = np.Inf
for epoch in range(1, n_epochs+1):
# initialize variables to monitor training and validation loss
train_loss = 0.0
valid_loss = 0.0
# set the module to training mode
model.train()
for batch_idx, (data, target) in enumerate(loaders['train']):
if use_cuda:
data, target = data.cuda(), target.cuda()
## TODO: find the loss and update the model parameters accordingly
## record the average training loss, using something like
## train_loss = train_loss + ((1 / (batch_idx + 1)) * (loss.data.item() - train_loss))
optimizer.zero_grad()
output = model(data)
loss = criterion(output, target)
loss.backward()
optimizer.step()
train_loss = train_loss + ((1 / (batch_idx + 1)) * (loss.data.item() - train_loss))
# set the model to evaluation mode
model.eval()
for batch_idx, (data, target) in enumerate(loaders['valid']):
if use_cuda:
data, target = data.cuda(), target.cuda()
## TODO: update average validation loss
output = model(data)
loss = criterion(output, target)
valid_loss = valid_loss + ((1 / (batch_idx + 1)) * (loss.data.item() - valid_loss))
print('Epoch: {} \tTraining Loss: {:.6f} \tValidation Loss: {:.6f}'.format(
epoch,
train_loss,
valid_loss
))
## TODO: if the validation loss has decreased, save the model at the filepath stored in save_path
if valid_loss <= valid_loss_min:
print('Validation loss decreased ({:.6f} --> {:.6f}). Saving model ...'.format(
valid_loss_min,
valid_loss))
torch.save(model.state_dict(), Path(save_path))
valid_loss_min = valid_loss
return model
Use the code cell below to define a custom weight initialization, and then train with your weight initialization for a few epochs. Make sure that neither the training loss nor validation loss is nan.
Later on, you will be able to see how this compares to training with PyTorch's default weight initialization.
def custom_weight_init(m):
## TODO: implement a weight initialization strategy
classname = m.__class__.__name__
if classname.find('Linear') != -1:
# get the number of the inputs
n = m.in_features
y = (1.0/np.sqrt(n))
m.weight.data.normal_(0, y)
m.bias.data.fill_(0)
#-#-# Do NOT modify the code below this line. #-#-#
model_scratch.apply(custom_weight_init)
model_scratch = train(20, loaders_scratch, model_scratch, get_optimizer_scratch(model_scratch),
criterion_scratch, use_cuda, 'ignore.pt')
Epoch: 1 Training Loss: 3.642952 Validation Loss: 3.383878 Validation loss decreased (inf --> 3.383878). Saving model ... Epoch: 2 Training Loss: 3.088747 Validation Loss: 3.097011 Validation loss decreased (3.383878 --> 3.097011). Saving model ... Epoch: 3 Training Loss: 2.760386 Validation Loss: 2.983191 Validation loss decreased (3.097011 --> 2.983191). Saving model ... Epoch: 4 Training Loss: 2.532411 Validation Loss: 3.013427 Epoch: 5 Training Loss: 2.322347 Validation Loss: 2.788637 Validation loss decreased (2.983191 --> 2.788637). Saving model ... Epoch: 6 Training Loss: 2.116668 Validation Loss: 2.938269 Epoch: 7 Training Loss: 1.996516 Validation Loss: 2.725371 Validation loss decreased (2.788637 --> 2.725371). Saving model ... Epoch: 8 Training Loss: 1.784829 Validation Loss: 2.669517 Validation loss decreased (2.725371 --> 2.669517). Saving model ... Epoch: 9 Training Loss: 1.700221 Validation Loss: 3.086528 Epoch: 10 Training Loss: 1.553839 Validation Loss: 2.719985 Epoch: 11 Training Loss: 1.394657 Validation Loss: 2.549430 Validation loss decreased (2.669517 --> 2.549430). Saving model ... Epoch: 12 Training Loss: 1.289202 Validation Loss: 2.731132 Epoch: 13 Training Loss: 1.171765 Validation Loss: 2.518885 Validation loss decreased (2.549430 --> 2.518885). Saving model ... Epoch: 14 Training Loss: 1.021903 Validation Loss: 2.765533 Epoch: 15 Training Loss: 0.948215 Validation Loss: 2.622133 Epoch: 16 Training Loss: 0.861791 Validation Loss: 2.562502 Epoch: 17 Training Loss: 0.770392 Validation Loss: 2.662514 Epoch: 18 Training Loss: 0.661775 Validation Loss: 2.581128 Epoch: 19 Training Loss: 0.615446 Validation Loss: 2.733441 Epoch: 20 Training Loss: 0.563144 Validation Loss: 2.734717
Run the next code cell to train your model.
## TODO: you may change the number of epochs if you'd like,
## but changing it is not required
num_epochs = 15
#-#-# Do NOT modify the code below this line. #-#-#
# function to re-initialize a model with pytorch's default weight initialization
def default_weight_init(m):
reset_parameters = getattr(m, 'reset_parameters', None)
if callable(reset_parameters):
m.reset_parameters()
# reset the model parameters
model_scratch.apply(default_weight_init)
# train the model
model_scratch = train(num_epochs, loaders_scratch, model_scratch, get_optimizer_scratch(model_scratch),
criterion_scratch, use_cuda, 'model_scratch.pt')
Epoch: 1 Training Loss: 3.615351 Validation Loss: 3.674714 Validation loss decreased (inf --> 3.674714). Saving model ... Epoch: 2 Training Loss: 3.128148 Validation Loss: 3.005902 Validation loss decreased (3.674714 --> 3.005902). Saving model ... Epoch: 3 Training Loss: 2.774843 Validation Loss: 3.436413 Epoch: 4 Training Loss: 2.542713 Validation Loss: 2.914615 Validation loss decreased (3.005902 --> 2.914615). Saving model ... Epoch: 5 Training Loss: 2.288881 Validation Loss: 2.793131 Validation loss decreased (2.914615 --> 2.793131). Saving model ... Epoch: 6 Training Loss: 2.093663 Validation Loss: 2.899013 Epoch: 7 Training Loss: 1.904985 Validation Loss: 2.834969 Epoch: 8 Training Loss: 1.741057 Validation Loss: 2.701155 Validation loss decreased (2.793131 --> 2.701155). Saving model ... Epoch: 9 Training Loss: 1.627149 Validation Loss: 2.570322 Validation loss decreased (2.701155 --> 2.570322). Saving model ... Epoch: 10 Training Loss: 1.450643 Validation Loss: 2.557937 Validation loss decreased (2.570322 --> 2.557937). Saving model ... Epoch: 11 Training Loss: 1.310470 Validation Loss: 2.678315 Epoch: 12 Training Loss: 1.167957 Validation Loss: 2.639006 Epoch: 13 Training Loss: 1.059719 Validation Loss: 2.645990 Epoch: 14 Training Loss: 0.962187 Validation Loss: 2.648861 Epoch: 15 Training Loss: 0.848725 Validation Loss: 2.591343
Run the code cell below to try out your model on the test dataset of landmark images. Run the code cell below to calculate and print the test loss and accuracy. Ensure that your test accuracy is greater than 20%.
def test(loaders, model, criterion, use_cuda):
# monitor test loss and accuracy
test_loss = 0.
correct = 0.
total = 0.
# set the module to evaluation mode
model.eval()
for batch_idx, (data, target) in enumerate(loaders['test']):
# move to GPU
if use_cuda:
data, target = data.cuda(), target.cuda()
# forward pass: compute predicted outputs by passing inputs to the model
output = model(data)
# calculate the loss
loss = criterion(output, target)
# update average test loss
test_loss = test_loss + ((1 / (batch_idx + 1)) * (loss.data.item() - test_loss))
# convert output probabilities to predicted class
pred = output.data.max(1, keepdim=True)[1]
# compare predictions to true label
correct += np.sum(np.squeeze(pred.eq(target.data.view_as(pred))).cpu().numpy())
total += data.size(0)
print('Test Loss: {:.6f}\n'.format(test_loss))
print('\nTest Accuracy: %2d%% (%2d/%2d)' % (
100. * correct / total, correct, total))
# load the model that got the best validation accuracy
model_scratch.load_state_dict(torch.load('model_scratch.pt'))
test(loaders_scratch, model_scratch, criterion_scratch, use_cuda)
Test Loss: 2.472728 Test Accuracy: 40% (510/1250)
You will now use transfer learning to create a CNN that can identify landmarks from images. Your CNN must attain at least 60% accuracy on the test set.
Use the code cell below to create three separate data loaders: one for training data, one for validation data, and one for test data. Randomly split the images located at landmark_images/train to create the train and validation data loaders, and use the images located at landmark_images/test to create the test data loader.
All three of your data loaders should be accessible via a dictionary named loaders_transfer. Your train data loader should be at loaders_transfer['train'], your validation data loader should be at loaders_transfer['valid'], and your test data loader should be at loaders_transfer['test'].
If you like, you are welcome to use the same data loaders from the previous step, when you created a CNN from scratch.
### TODO: Write data loaders for training, validation, and test sets
## Specify appropriate transforms, and batch_sizes
import torch
from torch.utils.data.dataloader import DataLoader
from torchvision.datasets import ImageFolder
from torchvision import transforms
import numpy as np
from torch.utils.data.sampler import SubsetRandomSampler
data_transform_transfer_train = transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.RandomRotation(20),
transforms.RandomResizedCrop(480,scale=(0.9,1.0)),
transforms.ToTensor(),
transforms.Normalize((0.5,0.5, 0.5), (0.5,0.5, 0.5))
])
data_transform_transfer_test = transforms.Compose([
transforms.Resize((480,480)),
transforms.ToTensor(),
transforms.Normalize((0.5,0.5, 0.5), (0.5,0.5, 0.5))
])
batch_size = 10
num_workers = 4
data_root = "./landmark_images"
train_data_transfer= ImageFolder(f'{data_root}/train',transform=data_transform_transfer_train)
test_data_transfer = ImageFolder(f'{data_root}/test',transform=data_transform_transfer_test)
# obtain training indices that will be used for validation
valid_size = 0.2
num_train = len(train_data)
indices = list(range(num_train))
np.random.shuffle(indices)
split = int(np.floor(valid_size * num_train))
train_idx, valid_idx = indices[split:], indices[:split]
train_sampler = SubsetRandomSampler(train_idx)
valid_sampler = SubsetRandomSampler(valid_idx)
loaders_transfer = {'train': DataLoader(train_data_transfer,batch_size=batch_size,num_workers=num_workers, sampler=train_sampler),
'valid': DataLoader(train_data_transfer,batch_size=batch_size,num_workers=num_workers, sampler=valid_sampler),
'test': DataLoader(test_data_transfer,batch_size=batch_size,num_workers=num_workers)
}
Use the next code cell to specify a loss function and optimizer. Save the chosen loss function as criterion_transfer, and fill in the function get_optimizer_transfer below.
## TODO: select loss function
criterion_transfer = nn.CrossEntropyLoss()
def get_optimizer_transfer(model, lr = 0.001):
## TODO: select and return optimizer
return torch.optim.Adam(model.classifier.parameters(), lr=lr)
Use transfer learning to create a CNN to classify images of landmarks. Use the code cell below, and save your initialized model as the variable model_transfer.
import torchvision.models as models
model_transfer = models.efficientnet_v2_m(weights=models.EfficientNet_V2_M_Weights)
model_transfer
c:\Users\roman.duris\PycharmProjects\nd101-c2-landmarks-starter\venv\lib\site-packages\torchvision\models\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=EfficientNet_V2_M_Weights.IMAGENET1K_V1`. You can also use `weights=EfficientNet_V2_M_Weights.DEFAULT` to get the most up-to-date weights. warnings.warn(msg)
EfficientNet(
(features): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(3, 24, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(1): BatchNorm2d(24, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(1): Sequential(
(0): FusedMBConv(
(block): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(24, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(24, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
)
(stochastic_depth): StochasticDepth(p=0.0, mode=row)
)
(1): FusedMBConv(
(block): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(24, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(24, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
)
(stochastic_depth): StochasticDepth(p=0.0035087719298245615, mode=row)
)
(2): FusedMBConv(
(block): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(24, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(24, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
)
(stochastic_depth): StochasticDepth(p=0.007017543859649123, mode=row)
)
)
(2): Sequential(
(0): FusedMBConv(
(block): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(24, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(1): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(1): Conv2dNormActivation(
(0): Conv2d(96, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(48, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
)
(stochastic_depth): StochasticDepth(p=0.010526315789473686, mode=row)
)
(1): FusedMBConv(
(block): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(48, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(1): Conv2dNormActivation(
(0): Conv2d(192, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(48, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
)
(stochastic_depth): StochasticDepth(p=0.014035087719298246, mode=row)
)
(2): FusedMBConv(
(block): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(48, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(1): Conv2dNormActivation(
(0): Conv2d(192, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(48, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
)
(stochastic_depth): StochasticDepth(p=0.017543859649122806, mode=row)
)
(3): FusedMBConv(
(block): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(48, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(1): Conv2dNormActivation(
(0): Conv2d(192, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(48, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
)
(stochastic_depth): StochasticDepth(p=0.02105263157894737, mode=row)
)
(4): FusedMBConv(
(block): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(48, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(1): Conv2dNormActivation(
(0): Conv2d(192, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(48, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
)
(stochastic_depth): StochasticDepth(p=0.024561403508771933, mode=row)
)
)
(3): Sequential(
(0): FusedMBConv(
(block): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(48, 192, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(1): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(1): Conv2dNormActivation(
(0): Conv2d(192, 80, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(80, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
)
(stochastic_depth): StochasticDepth(p=0.028070175438596492, mode=row)
)
(1): FusedMBConv(
(block): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(80, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(320, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(1): Conv2dNormActivation(
(0): Conv2d(320, 80, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(80, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
)
(stochastic_depth): StochasticDepth(p=0.031578947368421054, mode=row)
)
(2): FusedMBConv(
(block): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(80, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(320, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(1): Conv2dNormActivation(
(0): Conv2d(320, 80, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(80, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
)
(stochastic_depth): StochasticDepth(p=0.03508771929824561, mode=row)
)
(3): FusedMBConv(
(block): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(80, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(320, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(1): Conv2dNormActivation(
(0): Conv2d(320, 80, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(80, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
)
(stochastic_depth): StochasticDepth(p=0.03859649122807018, mode=row)
)
(4): FusedMBConv(
(block): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(80, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(1): BatchNorm2d(320, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(1): Conv2dNormActivation(
(0): Conv2d(320, 80, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(80, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
)
(stochastic_depth): StochasticDepth(p=0.04210526315789474, mode=row)
)
)
(4): Sequential(
(0): MBConv(
(block): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(80, 320, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(320, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(1): Conv2dNormActivation(
(0): Conv2d(320, 320, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=320, bias=False)
(1): BatchNorm2d(320, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(2): SqueezeExcitation(
(avgpool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(320, 20, kernel_size=(1, 1), stride=(1, 1))
(fc2): Conv2d(20, 320, kernel_size=(1, 1), stride=(1, 1))
(activation): SiLU(inplace=True)
(scale_activation): Sigmoid()
)
(3): Conv2dNormActivation(
(0): Conv2d(320, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
)
(stochastic_depth): StochasticDepth(p=0.0456140350877193, mode=row)
)
(1): MBConv(
(block): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(160, 640, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(640, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(1): Conv2dNormActivation(
(0): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=640, bias=False)
(1): BatchNorm2d(640, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(2): SqueezeExcitation(
(avgpool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(640, 40, kernel_size=(1, 1), stride=(1, 1))
(fc2): Conv2d(40, 640, kernel_size=(1, 1), stride=(1, 1))
(activation): SiLU(inplace=True)
(scale_activation): Sigmoid()
)
(3): Conv2dNormActivation(
(0): Conv2d(640, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
)
(stochastic_depth): StochasticDepth(p=0.04912280701754387, mode=row)
)
(2): MBConv(
(block): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(160, 640, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(640, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(1): Conv2dNormActivation(
(0): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=640, bias=False)
(1): BatchNorm2d(640, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(2): SqueezeExcitation(
(avgpool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(640, 40, kernel_size=(1, 1), stride=(1, 1))
(fc2): Conv2d(40, 640, kernel_size=(1, 1), stride=(1, 1))
(activation): SiLU(inplace=True)
(scale_activation): Sigmoid()
)
(3): Conv2dNormActivation(
(0): Conv2d(640, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
)
(stochastic_depth): StochasticDepth(p=0.05263157894736842, mode=row)
)
(3): MBConv(
(block): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(160, 640, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(640, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(1): Conv2dNormActivation(
(0): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=640, bias=False)
(1): BatchNorm2d(640, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(2): SqueezeExcitation(
(avgpool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(640, 40, kernel_size=(1, 1), stride=(1, 1))
(fc2): Conv2d(40, 640, kernel_size=(1, 1), stride=(1, 1))
(activation): SiLU(inplace=True)
(scale_activation): Sigmoid()
)
(3): Conv2dNormActivation(
(0): Conv2d(640, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
)
(stochastic_depth): StochasticDepth(p=0.056140350877192984, mode=row)
)
(4): MBConv(
(block): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(160, 640, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(640, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(1): Conv2dNormActivation(
(0): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=640, bias=False)
(1): BatchNorm2d(640, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(2): SqueezeExcitation(
(avgpool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(640, 40, kernel_size=(1, 1), stride=(1, 1))
(fc2): Conv2d(40, 640, kernel_size=(1, 1), stride=(1, 1))
(activation): SiLU(inplace=True)
(scale_activation): Sigmoid()
)
(3): Conv2dNormActivation(
(0): Conv2d(640, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
)
(stochastic_depth): StochasticDepth(p=0.05964912280701755, mode=row)
)
(5): MBConv(
(block): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(160, 640, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(640, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(1): Conv2dNormActivation(
(0): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=640, bias=False)
(1): BatchNorm2d(640, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(2): SqueezeExcitation(
(avgpool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(640, 40, kernel_size=(1, 1), stride=(1, 1))
(fc2): Conv2d(40, 640, kernel_size=(1, 1), stride=(1, 1))
(activation): SiLU(inplace=True)
(scale_activation): Sigmoid()
)
(3): Conv2dNormActivation(
(0): Conv2d(640, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
)
(stochastic_depth): StochasticDepth(p=0.06315789473684211, mode=row)
)
(6): MBConv(
(block): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(160, 640, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(640, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(1): Conv2dNormActivation(
(0): Conv2d(640, 640, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=640, bias=False)
(1): BatchNorm2d(640, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(2): SqueezeExcitation(
(avgpool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(640, 40, kernel_size=(1, 1), stride=(1, 1))
(fc2): Conv2d(40, 640, kernel_size=(1, 1), stride=(1, 1))
(activation): SiLU(inplace=True)
(scale_activation): Sigmoid()
)
(3): Conv2dNormActivation(
(0): Conv2d(640, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
)
(stochastic_depth): StochasticDepth(p=0.06666666666666667, mode=row)
)
)
(5): Sequential(
(0): MBConv(
(block): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(960, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(1): Conv2dNormActivation(
(0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)
(1): BatchNorm2d(960, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(2): SqueezeExcitation(
(avgpool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(960, 40, kernel_size=(1, 1), stride=(1, 1))
(fc2): Conv2d(40, 960, kernel_size=(1, 1), stride=(1, 1))
(activation): SiLU(inplace=True)
(scale_activation): Sigmoid()
)
(3): Conv2dNormActivation(
(0): Conv2d(960, 176, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(176, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
)
(stochastic_depth): StochasticDepth(p=0.07017543859649122, mode=row)
)
(1): MBConv(
(block): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(176, 1056, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(1056, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(1): Conv2dNormActivation(
(0): Conv2d(1056, 1056, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1056, bias=False)
(1): BatchNorm2d(1056, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(2): SqueezeExcitation(
(avgpool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(1056, 44, kernel_size=(1, 1), stride=(1, 1))
(fc2): Conv2d(44, 1056, kernel_size=(1, 1), stride=(1, 1))
(activation): SiLU(inplace=True)
(scale_activation): Sigmoid()
)
(3): Conv2dNormActivation(
(0): Conv2d(1056, 176, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(176, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
)
(stochastic_depth): StochasticDepth(p=0.0736842105263158, mode=row)
)
(2): MBConv(
(block): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(176, 1056, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(1056, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(1): Conv2dNormActivation(
(0): Conv2d(1056, 1056, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1056, bias=False)
(1): BatchNorm2d(1056, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(2): SqueezeExcitation(
(avgpool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(1056, 44, kernel_size=(1, 1), stride=(1, 1))
(fc2): Conv2d(44, 1056, kernel_size=(1, 1), stride=(1, 1))
(activation): SiLU(inplace=True)
(scale_activation): Sigmoid()
)
(3): Conv2dNormActivation(
(0): Conv2d(1056, 176, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(176, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
)
(stochastic_depth): StochasticDepth(p=0.07719298245614035, mode=row)
)
(3): MBConv(
(block): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(176, 1056, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(1056, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(1): Conv2dNormActivation(
(0): Conv2d(1056, 1056, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1056, bias=False)
(1): BatchNorm2d(1056, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(2): SqueezeExcitation(
(avgpool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(1056, 44, kernel_size=(1, 1), stride=(1, 1))
(fc2): Conv2d(44, 1056, kernel_size=(1, 1), stride=(1, 1))
(activation): SiLU(inplace=True)
(scale_activation): Sigmoid()
)
(3): Conv2dNormActivation(
(0): Conv2d(1056, 176, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(176, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
)
(stochastic_depth): StochasticDepth(p=0.08070175438596493, mode=row)
)
(4): MBConv(
(block): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(176, 1056, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(1056, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(1): Conv2dNormActivation(
(0): Conv2d(1056, 1056, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1056, bias=False)
(1): BatchNorm2d(1056, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(2): SqueezeExcitation(
(avgpool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(1056, 44, kernel_size=(1, 1), stride=(1, 1))
(fc2): Conv2d(44, 1056, kernel_size=(1, 1), stride=(1, 1))
(activation): SiLU(inplace=True)
(scale_activation): Sigmoid()
)
(3): Conv2dNormActivation(
(0): Conv2d(1056, 176, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(176, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
)
(stochastic_depth): StochasticDepth(p=0.08421052631578949, mode=row)
)
(5): MBConv(
(block): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(176, 1056, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(1056, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(1): Conv2dNormActivation(
(0): Conv2d(1056, 1056, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1056, bias=False)
(1): BatchNorm2d(1056, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(2): SqueezeExcitation(
(avgpool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(1056, 44, kernel_size=(1, 1), stride=(1, 1))
(fc2): Conv2d(44, 1056, kernel_size=(1, 1), stride=(1, 1))
(activation): SiLU(inplace=True)
(scale_activation): Sigmoid()
)
(3): Conv2dNormActivation(
(0): Conv2d(1056, 176, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(176, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
)
(stochastic_depth): StochasticDepth(p=0.08771929824561403, mode=row)
)
(6): MBConv(
(block): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(176, 1056, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(1056, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(1): Conv2dNormActivation(
(0): Conv2d(1056, 1056, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1056, bias=False)
(1): BatchNorm2d(1056, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(2): SqueezeExcitation(
(avgpool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(1056, 44, kernel_size=(1, 1), stride=(1, 1))
(fc2): Conv2d(44, 1056, kernel_size=(1, 1), stride=(1, 1))
(activation): SiLU(inplace=True)
(scale_activation): Sigmoid()
)
(3): Conv2dNormActivation(
(0): Conv2d(1056, 176, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(176, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
)
(stochastic_depth): StochasticDepth(p=0.0912280701754386, mode=row)
)
(7): MBConv(
(block): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(176, 1056, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(1056, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(1): Conv2dNormActivation(
(0): Conv2d(1056, 1056, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1056, bias=False)
(1): BatchNorm2d(1056, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(2): SqueezeExcitation(
(avgpool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(1056, 44, kernel_size=(1, 1), stride=(1, 1))
(fc2): Conv2d(44, 1056, kernel_size=(1, 1), stride=(1, 1))
(activation): SiLU(inplace=True)
(scale_activation): Sigmoid()
)
(3): Conv2dNormActivation(
(0): Conv2d(1056, 176, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(176, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
)
(stochastic_depth): StochasticDepth(p=0.09473684210526316, mode=row)
)
(8): MBConv(
(block): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(176, 1056, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(1056, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(1): Conv2dNormActivation(
(0): Conv2d(1056, 1056, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1056, bias=False)
(1): BatchNorm2d(1056, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(2): SqueezeExcitation(
(avgpool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(1056, 44, kernel_size=(1, 1), stride=(1, 1))
(fc2): Conv2d(44, 1056, kernel_size=(1, 1), stride=(1, 1))
(activation): SiLU(inplace=True)
(scale_activation): Sigmoid()
)
(3): Conv2dNormActivation(
(0): Conv2d(1056, 176, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(176, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
)
(stochastic_depth): StochasticDepth(p=0.09824561403508773, mode=row)
)
(9): MBConv(
(block): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(176, 1056, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(1056, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(1): Conv2dNormActivation(
(0): Conv2d(1056, 1056, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1056, bias=False)
(1): BatchNorm2d(1056, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(2): SqueezeExcitation(
(avgpool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(1056, 44, kernel_size=(1, 1), stride=(1, 1))
(fc2): Conv2d(44, 1056, kernel_size=(1, 1), stride=(1, 1))
(activation): SiLU(inplace=True)
(scale_activation): Sigmoid()
)
(3): Conv2dNormActivation(
(0): Conv2d(1056, 176, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(176, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
)
(stochastic_depth): StochasticDepth(p=0.10175438596491229, mode=row)
)
(10): MBConv(
(block): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(176, 1056, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(1056, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(1): Conv2dNormActivation(
(0): Conv2d(1056, 1056, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1056, bias=False)
(1): BatchNorm2d(1056, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(2): SqueezeExcitation(
(avgpool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(1056, 44, kernel_size=(1, 1), stride=(1, 1))
(fc2): Conv2d(44, 1056, kernel_size=(1, 1), stride=(1, 1))
(activation): SiLU(inplace=True)
(scale_activation): Sigmoid()
)
(3): Conv2dNormActivation(
(0): Conv2d(1056, 176, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(176, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
)
(stochastic_depth): StochasticDepth(p=0.10526315789473684, mode=row)
)
(11): MBConv(
(block): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(176, 1056, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(1056, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(1): Conv2dNormActivation(
(0): Conv2d(1056, 1056, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1056, bias=False)
(1): BatchNorm2d(1056, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(2): SqueezeExcitation(
(avgpool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(1056, 44, kernel_size=(1, 1), stride=(1, 1))
(fc2): Conv2d(44, 1056, kernel_size=(1, 1), stride=(1, 1))
(activation): SiLU(inplace=True)
(scale_activation): Sigmoid()
)
(3): Conv2dNormActivation(
(0): Conv2d(1056, 176, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(176, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
)
(stochastic_depth): StochasticDepth(p=0.10877192982456141, mode=row)
)
(12): MBConv(
(block): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(176, 1056, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(1056, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(1): Conv2dNormActivation(
(0): Conv2d(1056, 1056, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1056, bias=False)
(1): BatchNorm2d(1056, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(2): SqueezeExcitation(
(avgpool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(1056, 44, kernel_size=(1, 1), stride=(1, 1))
(fc2): Conv2d(44, 1056, kernel_size=(1, 1), stride=(1, 1))
(activation): SiLU(inplace=True)
(scale_activation): Sigmoid()
)
(3): Conv2dNormActivation(
(0): Conv2d(1056, 176, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(176, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
)
(stochastic_depth): StochasticDepth(p=0.11228070175438597, mode=row)
)
(13): MBConv(
(block): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(176, 1056, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(1056, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(1): Conv2dNormActivation(
(0): Conv2d(1056, 1056, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1056, bias=False)
(1): BatchNorm2d(1056, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(2): SqueezeExcitation(
(avgpool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(1056, 44, kernel_size=(1, 1), stride=(1, 1))
(fc2): Conv2d(44, 1056, kernel_size=(1, 1), stride=(1, 1))
(activation): SiLU(inplace=True)
(scale_activation): Sigmoid()
)
(3): Conv2dNormActivation(
(0): Conv2d(1056, 176, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(176, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
)
(stochastic_depth): StochasticDepth(p=0.11578947368421054, mode=row)
)
)
(6): Sequential(
(0): MBConv(
(block): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(176, 1056, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(1056, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(1): Conv2dNormActivation(
(0): Conv2d(1056, 1056, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=1056, bias=False)
(1): BatchNorm2d(1056, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(2): SqueezeExcitation(
(avgpool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(1056, 44, kernel_size=(1, 1), stride=(1, 1))
(fc2): Conv2d(44, 1056, kernel_size=(1, 1), stride=(1, 1))
(activation): SiLU(inplace=True)
(scale_activation): Sigmoid()
)
(3): Conv2dNormActivation(
(0): Conv2d(1056, 304, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(304, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
)
(stochastic_depth): StochasticDepth(p=0.1192982456140351, mode=row)
)
(1): MBConv(
(block): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(304, 1824, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(1): Conv2dNormActivation(
(0): Conv2d(1824, 1824, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1824, bias=False)
(1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(2): SqueezeExcitation(
(avgpool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(1824, 76, kernel_size=(1, 1), stride=(1, 1))
(fc2): Conv2d(76, 1824, kernel_size=(1, 1), stride=(1, 1))
(activation): SiLU(inplace=True)
(scale_activation): Sigmoid()
)
(3): Conv2dNormActivation(
(0): Conv2d(1824, 304, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(304, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
)
(stochastic_depth): StochasticDepth(p=0.12280701754385964, mode=row)
)
(2): MBConv(
(block): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(304, 1824, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(1): Conv2dNormActivation(
(0): Conv2d(1824, 1824, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1824, bias=False)
(1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(2): SqueezeExcitation(
(avgpool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(1824, 76, kernel_size=(1, 1), stride=(1, 1))
(fc2): Conv2d(76, 1824, kernel_size=(1, 1), stride=(1, 1))
(activation): SiLU(inplace=True)
(scale_activation): Sigmoid()
)
(3): Conv2dNormActivation(
(0): Conv2d(1824, 304, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(304, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
)
(stochastic_depth): StochasticDepth(p=0.12631578947368421, mode=row)
)
(3): MBConv(
(block): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(304, 1824, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(1): Conv2dNormActivation(
(0): Conv2d(1824, 1824, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1824, bias=False)
(1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(2): SqueezeExcitation(
(avgpool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(1824, 76, kernel_size=(1, 1), stride=(1, 1))
(fc2): Conv2d(76, 1824, kernel_size=(1, 1), stride=(1, 1))
(activation): SiLU(inplace=True)
(scale_activation): Sigmoid()
)
(3): Conv2dNormActivation(
(0): Conv2d(1824, 304, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(304, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
)
(stochastic_depth): StochasticDepth(p=0.1298245614035088, mode=row)
)
(4): MBConv(
(block): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(304, 1824, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(1): Conv2dNormActivation(
(0): Conv2d(1824, 1824, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1824, bias=False)
(1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(2): SqueezeExcitation(
(avgpool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(1824, 76, kernel_size=(1, 1), stride=(1, 1))
(fc2): Conv2d(76, 1824, kernel_size=(1, 1), stride=(1, 1))
(activation): SiLU(inplace=True)
(scale_activation): Sigmoid()
)
(3): Conv2dNormActivation(
(0): Conv2d(1824, 304, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(304, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
)
(stochastic_depth): StochasticDepth(p=0.13333333333333333, mode=row)
)
(5): MBConv(
(block): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(304, 1824, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(1): Conv2dNormActivation(
(0): Conv2d(1824, 1824, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1824, bias=False)
(1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(2): SqueezeExcitation(
(avgpool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(1824, 76, kernel_size=(1, 1), stride=(1, 1))
(fc2): Conv2d(76, 1824, kernel_size=(1, 1), stride=(1, 1))
(activation): SiLU(inplace=True)
(scale_activation): Sigmoid()
)
(3): Conv2dNormActivation(
(0): Conv2d(1824, 304, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(304, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
)
(stochastic_depth): StochasticDepth(p=0.1368421052631579, mode=row)
)
(6): MBConv(
(block): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(304, 1824, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(1): Conv2dNormActivation(
(0): Conv2d(1824, 1824, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1824, bias=False)
(1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(2): SqueezeExcitation(
(avgpool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(1824, 76, kernel_size=(1, 1), stride=(1, 1))
(fc2): Conv2d(76, 1824, kernel_size=(1, 1), stride=(1, 1))
(activation): SiLU(inplace=True)
(scale_activation): Sigmoid()
)
(3): Conv2dNormActivation(
(0): Conv2d(1824, 304, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(304, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
)
(stochastic_depth): StochasticDepth(p=0.14035087719298245, mode=row)
)
(7): MBConv(
(block): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(304, 1824, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(1): Conv2dNormActivation(
(0): Conv2d(1824, 1824, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1824, bias=False)
(1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(2): SqueezeExcitation(
(avgpool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(1824, 76, kernel_size=(1, 1), stride=(1, 1))
(fc2): Conv2d(76, 1824, kernel_size=(1, 1), stride=(1, 1))
(activation): SiLU(inplace=True)
(scale_activation): Sigmoid()
)
(3): Conv2dNormActivation(
(0): Conv2d(1824, 304, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(304, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
)
(stochastic_depth): StochasticDepth(p=0.14385964912280705, mode=row)
)
(8): MBConv(
(block): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(304, 1824, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(1): Conv2dNormActivation(
(0): Conv2d(1824, 1824, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1824, bias=False)
(1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(2): SqueezeExcitation(
(avgpool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(1824, 76, kernel_size=(1, 1), stride=(1, 1))
(fc2): Conv2d(76, 1824, kernel_size=(1, 1), stride=(1, 1))
(activation): SiLU(inplace=True)
(scale_activation): Sigmoid()
)
(3): Conv2dNormActivation(
(0): Conv2d(1824, 304, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(304, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
)
(stochastic_depth): StochasticDepth(p=0.1473684210526316, mode=row)
)
(9): MBConv(
(block): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(304, 1824, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(1): Conv2dNormActivation(
(0): Conv2d(1824, 1824, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1824, bias=False)
(1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(2): SqueezeExcitation(
(avgpool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(1824, 76, kernel_size=(1, 1), stride=(1, 1))
(fc2): Conv2d(76, 1824, kernel_size=(1, 1), stride=(1, 1))
(activation): SiLU(inplace=True)
(scale_activation): Sigmoid()
)
(3): Conv2dNormActivation(
(0): Conv2d(1824, 304, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(304, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
)
(stochastic_depth): StochasticDepth(p=0.15087719298245614, mode=row)
)
(10): MBConv(
(block): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(304, 1824, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(1): Conv2dNormActivation(
(0): Conv2d(1824, 1824, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1824, bias=False)
(1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(2): SqueezeExcitation(
(avgpool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(1824, 76, kernel_size=(1, 1), stride=(1, 1))
(fc2): Conv2d(76, 1824, kernel_size=(1, 1), stride=(1, 1))
(activation): SiLU(inplace=True)
(scale_activation): Sigmoid()
)
(3): Conv2dNormActivation(
(0): Conv2d(1824, 304, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(304, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
)
(stochastic_depth): StochasticDepth(p=0.1543859649122807, mode=row)
)
(11): MBConv(
(block): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(304, 1824, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(1): Conv2dNormActivation(
(0): Conv2d(1824, 1824, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1824, bias=False)
(1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(2): SqueezeExcitation(
(avgpool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(1824, 76, kernel_size=(1, 1), stride=(1, 1))
(fc2): Conv2d(76, 1824, kernel_size=(1, 1), stride=(1, 1))
(activation): SiLU(inplace=True)
(scale_activation): Sigmoid()
)
(3): Conv2dNormActivation(
(0): Conv2d(1824, 304, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(304, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
)
(stochastic_depth): StochasticDepth(p=0.15789473684210525, mode=row)
)
(12): MBConv(
(block): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(304, 1824, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(1): Conv2dNormActivation(
(0): Conv2d(1824, 1824, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1824, bias=False)
(1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(2): SqueezeExcitation(
(avgpool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(1824, 76, kernel_size=(1, 1), stride=(1, 1))
(fc2): Conv2d(76, 1824, kernel_size=(1, 1), stride=(1, 1))
(activation): SiLU(inplace=True)
(scale_activation): Sigmoid()
)
(3): Conv2dNormActivation(
(0): Conv2d(1824, 304, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(304, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
)
(stochastic_depth): StochasticDepth(p=0.16140350877192985, mode=row)
)
(13): MBConv(
(block): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(304, 1824, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(1): Conv2dNormActivation(
(0): Conv2d(1824, 1824, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1824, bias=False)
(1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(2): SqueezeExcitation(
(avgpool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(1824, 76, kernel_size=(1, 1), stride=(1, 1))
(fc2): Conv2d(76, 1824, kernel_size=(1, 1), stride=(1, 1))
(activation): SiLU(inplace=True)
(scale_activation): Sigmoid()
)
(3): Conv2dNormActivation(
(0): Conv2d(1824, 304, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(304, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
)
(stochastic_depth): StochasticDepth(p=0.1649122807017544, mode=row)
)
(14): MBConv(
(block): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(304, 1824, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(1): Conv2dNormActivation(
(0): Conv2d(1824, 1824, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1824, bias=False)
(1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(2): SqueezeExcitation(
(avgpool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(1824, 76, kernel_size=(1, 1), stride=(1, 1))
(fc2): Conv2d(76, 1824, kernel_size=(1, 1), stride=(1, 1))
(activation): SiLU(inplace=True)
(scale_activation): Sigmoid()
)
(3): Conv2dNormActivation(
(0): Conv2d(1824, 304, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(304, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
)
(stochastic_depth): StochasticDepth(p=0.16842105263157897, mode=row)
)
(15): MBConv(
(block): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(304, 1824, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(1): Conv2dNormActivation(
(0): Conv2d(1824, 1824, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1824, bias=False)
(1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(2): SqueezeExcitation(
(avgpool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(1824, 76, kernel_size=(1, 1), stride=(1, 1))
(fc2): Conv2d(76, 1824, kernel_size=(1, 1), stride=(1, 1))
(activation): SiLU(inplace=True)
(scale_activation): Sigmoid()
)
(3): Conv2dNormActivation(
(0): Conv2d(1824, 304, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(304, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
)
(stochastic_depth): StochasticDepth(p=0.17192982456140352, mode=row)
)
(16): MBConv(
(block): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(304, 1824, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(1): Conv2dNormActivation(
(0): Conv2d(1824, 1824, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1824, bias=False)
(1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(2): SqueezeExcitation(
(avgpool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(1824, 76, kernel_size=(1, 1), stride=(1, 1))
(fc2): Conv2d(76, 1824, kernel_size=(1, 1), stride=(1, 1))
(activation): SiLU(inplace=True)
(scale_activation): Sigmoid()
)
(3): Conv2dNormActivation(
(0): Conv2d(1824, 304, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(304, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
)
(stochastic_depth): StochasticDepth(p=0.17543859649122806, mode=row)
)
(17): MBConv(
(block): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(304, 1824, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(1): Conv2dNormActivation(
(0): Conv2d(1824, 1824, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1824, bias=False)
(1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(2): SqueezeExcitation(
(avgpool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(1824, 76, kernel_size=(1, 1), stride=(1, 1))
(fc2): Conv2d(76, 1824, kernel_size=(1, 1), stride=(1, 1))
(activation): SiLU(inplace=True)
(scale_activation): Sigmoid()
)
(3): Conv2dNormActivation(
(0): Conv2d(1824, 304, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(304, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
)
(stochastic_depth): StochasticDepth(p=0.17894736842105266, mode=row)
)
)
(7): Sequential(
(0): MBConv(
(block): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(304, 1824, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(1): Conv2dNormActivation(
(0): Conv2d(1824, 1824, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1824, bias=False)
(1): BatchNorm2d(1824, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(2): SqueezeExcitation(
(avgpool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(1824, 76, kernel_size=(1, 1), stride=(1, 1))
(fc2): Conv2d(76, 1824, kernel_size=(1, 1), stride=(1, 1))
(activation): SiLU(inplace=True)
(scale_activation): Sigmoid()
)
(3): Conv2dNormActivation(
(0): Conv2d(1824, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(512, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
)
(stochastic_depth): StochasticDepth(p=0.1824561403508772, mode=row)
)
(1): MBConv(
(block): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(512, 3072, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(3072, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(1): Conv2dNormActivation(
(0): Conv2d(3072, 3072, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=3072, bias=False)
(1): BatchNorm2d(3072, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(2): SqueezeExcitation(
(avgpool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(3072, 128, kernel_size=(1, 1), stride=(1, 1))
(fc2): Conv2d(128, 3072, kernel_size=(1, 1), stride=(1, 1))
(activation): SiLU(inplace=True)
(scale_activation): Sigmoid()
)
(3): Conv2dNormActivation(
(0): Conv2d(3072, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(512, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
)
(stochastic_depth): StochasticDepth(p=0.18596491228070178, mode=row)
)
(2): MBConv(
(block): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(512, 3072, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(3072, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(1): Conv2dNormActivation(
(0): Conv2d(3072, 3072, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=3072, bias=False)
(1): BatchNorm2d(3072, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(2): SqueezeExcitation(
(avgpool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(3072, 128, kernel_size=(1, 1), stride=(1, 1))
(fc2): Conv2d(128, 3072, kernel_size=(1, 1), stride=(1, 1))
(activation): SiLU(inplace=True)
(scale_activation): Sigmoid()
)
(3): Conv2dNormActivation(
(0): Conv2d(3072, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(512, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
)
(stochastic_depth): StochasticDepth(p=0.18947368421052632, mode=row)
)
(3): MBConv(
(block): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(512, 3072, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(3072, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(1): Conv2dNormActivation(
(0): Conv2d(3072, 3072, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=3072, bias=False)
(1): BatchNorm2d(3072, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(2): SqueezeExcitation(
(avgpool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(3072, 128, kernel_size=(1, 1), stride=(1, 1))
(fc2): Conv2d(128, 3072, kernel_size=(1, 1), stride=(1, 1))
(activation): SiLU(inplace=True)
(scale_activation): Sigmoid()
)
(3): Conv2dNormActivation(
(0): Conv2d(3072, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(512, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
)
(stochastic_depth): StochasticDepth(p=0.19298245614035087, mode=row)
)
(4): MBConv(
(block): Sequential(
(0): Conv2dNormActivation(
(0): Conv2d(512, 3072, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(3072, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(1): Conv2dNormActivation(
(0): Conv2d(3072, 3072, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=3072, bias=False)
(1): BatchNorm2d(3072, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
(2): SqueezeExcitation(
(avgpool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(3072, 128, kernel_size=(1, 1), stride=(1, 1))
(fc2): Conv2d(128, 3072, kernel_size=(1, 1), stride=(1, 1))
(activation): SiLU(inplace=True)
(scale_activation): Sigmoid()
)
(3): Conv2dNormActivation(
(0): Conv2d(3072, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(512, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
)
)
(stochastic_depth): StochasticDepth(p=0.19649122807017547, mode=row)
)
)
(8): Conv2dNormActivation(
(0): Conv2d(512, 1280, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(1280, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)
(2): SiLU(inplace=True)
)
)
(avgpool): AdaptiveAvgPool2d(output_size=1)
(classifier): Sequential(
(0): Dropout(p=0.3, inplace=True)
(1): Linear(in_features=1280, out_features=1000, bias=True)
)
)
import torchvision.models as models
def get_transfer_model(out_channels):
model_transfer = models.efficientnet_v2_m(weights=models.EfficientNet_V2_M_Weights.IMAGENET1K_V1)
model_transfer
for param in model_transfer.features.parameters():
param.requires_grad = False
n_inputs = model_transfer.classifier[1].in_features
# add last linear layer (n_inputs -> 5 flower classes)
# new layers automatically have requires_grad = True
last_layer = nn.Linear(n_inputs, out_channels)
model_transfer.classifier[1] = last_layer
# if GPU is available, move the model to GPU
if use_cuda:
model_transfer.cuda()
# check to see that your last layer produces the expected number of outputs
print(model_transfer.classifier[1].out_features)
return model_transfer
## TODO: Specify model architecture
model_transfer = get_transfer_model(50)
#-#-# Do NOT modify the code below this line. #-#-#
if use_cuda:
model_transfer = model_transfer.cuda()
50
Question 3: Outline the steps you took to get to your final CNN architecture and your reasoning at each step. Describe why you think the architecture is suitable for the current problem.
Answer:
Train and validate your model in the code cell below. Save the final model parameters at filepath 'model_transfer.pt'.
# TODO: train the model and save the best model parameters at filepath 'model_transfer.pt'
from pathlib import Path
def train_transfer(n_epochs, loaders, model, optimizer, criterion, use_cuda, save_path):
"""returns trained model"""
# initialize tracker for minimum validation loss
valid_loss_min = np.Inf
for epoch in range(1, n_epochs+1):
# initialize variables to monitor training and validation loss
train_loss = 0.0
valid_loss = 0.0
model.train()
for batch_idx, (data, target) in enumerate(loaders['train']):
if use_cuda:
data, target = data.cuda(), target.cuda()
## TODO: find the loss and update the model parameters accordingly
## record the average training loss, using something like
## train_loss = train_loss + ((1 / (batch_idx + 1)) * (loss.data.item() - train_loss))
optimizer.zero_grad()
output = model(data)
loss = criterion(output, target)
# backward pass: compute gradient of the loss with respect to model parameters
loss.backward()
# perform a single optimization step (parameter update)
optimizer.step()
# update training loss
train_loss = train_loss + ((1 / (batch_idx + 1)) * (loss.data.item() - train_loss))
model.eval()
for batch_idx, (data, target) in enumerate(loaders['valid']):
if use_cuda:
data, target = data.cuda(), target.cuda()
## TODO: update average validation loss
output = model(data)
loss = criterion(output, target)
valid_loss = valid_loss + ((1 / (batch_idx + 1)) * (loss.data.item() - valid_loss))
# print training/validation statistics
print('Epoch: {} \tTraining Loss: {:.6f} \tValidation Loss: {:.6f}'.format(
epoch,
train_loss,
valid_loss
))
## TODO: if the validation loss has decreased, save the model at the filepath stored in save_path
if valid_loss <= valid_loss_min:
print('Validation loss decreased ({:.6f} --> {:.6f}). Saving model ...'.format(
valid_loss_min,
valid_loss))
torch.save(model.state_dict(), Path(save_path))
valid_loss_min = valid_loss
return model
train_transfer(20,loaders_transfer,model_transfer,get_optimizer_transfer(model_transfer),criterion_transfer,use_cuda,'model_transfer.pt')
#-#-# Do NOT modify the code below this line. #-#-#
# load the model that got the best validation accuracy
model_transfer.load_state_dict(torch.load('model_transfer.pt'))
Epoch: 1 Training Loss: 2.136675 Validation Loss: 1.324377 Validation loss decreased (inf --> 1.324377). Saving model ... Epoch: 2 Training Loss: 1.156292 Validation Loss: 1.038303 Validation loss decreased (1.324377 --> 1.038303). Saving model ... Epoch: 3 Training Loss: 0.916203 Validation Loss: 0.889128 Validation loss decreased (1.038303 --> 0.889128). Saving model ... Epoch: 4 Training Loss: 0.809105 Validation Loss: 0.838760 Validation loss decreased (0.889128 --> 0.838760). Saving model ... Epoch: 5 Training Loss: 0.715268 Validation Loss: 0.796972 Validation loss decreased (0.838760 --> 0.796972). Saving model ... Epoch: 6 Training Loss: 0.637793 Validation Loss: 0.779221 Validation loss decreased (0.796972 --> 0.779221). Saving model ... Epoch: 7 Training Loss: 0.631772 Validation Loss: 0.762187 Validation loss decreased (0.779221 --> 0.762187). Saving model ... Epoch: 8 Training Loss: 0.575847 Validation Loss: 0.713691 Validation loss decreased (0.762187 --> 0.713691). Saving model ... Epoch: 9 Training Loss: 0.551050 Validation Loss: 0.727864 Epoch: 10 Training Loss: 0.511181 Validation Loss: 0.708241 Validation loss decreased (0.713691 --> 0.708241). Saving model ... Epoch: 11 Training Loss: 0.490719 Validation Loss: 0.721765 Epoch: 12 Training Loss: 0.469851 Validation Loss: 0.724767 Epoch: 13 Training Loss: 0.444384 Validation Loss: 0.739627 Epoch: 14 Training Loss: 0.434966 Validation Loss: 0.716787 Epoch: 15 Training Loss: 0.421902 Validation Loss: 0.728361 Epoch: 16 Training Loss: 0.401493 Validation Loss: 0.697946 Validation loss decreased (0.708241 --> 0.697946). Saving model ... Epoch: 17 Training Loss: 0.391713 Validation Loss: 0.690725 Validation loss decreased (0.697946 --> 0.690725). Saving model ... Epoch: 18 Training Loss: 0.398902 Validation Loss: 0.695999 Epoch: 19 Training Loss: 0.383941 Validation Loss: 0.687852 Validation loss decreased (0.690725 --> 0.687852). Saving model ... Epoch: 20 Training Loss: 0.371122 Validation Loss: 0.655480 Validation loss decreased (0.687852 --> 0.655480). Saving model ...
<All keys matched successfully>
Try out your model on the test dataset of landmark images. Use the code cell below to calculate and print the test loss and accuracy. Ensure that your test accuracy is greater than 60%.
model_transfer.load_state_dict(torch.load('model_transfer.pt'))
test(loaders_transfer, model_transfer, criterion_transfer, use_cuda)
Test Loss: 0.600792 Test Accuracy: 83% (1038/1250)
Great job creating your CNN models! Now that you have put in all the hard work of creating accurate classifiers, let's define some functions to make it easy for others to use your classifiers.
Implement the function predict_landmarks, which accepts a file path to an image and an integer k, and then predicts the top k most likely landmarks. You are required to use your transfer learned CNN from Step 2 to predict the landmarks.
An example of the expected behavior of predict_landmarks:
>>> predicted_landmarks = predict_landmarks('example_image.jpg', 3)
>>> print(predicted_landmarks)
['Golden Gate Bridge', 'Brooklyn Bridge', 'Sydney Harbour Bridge']
import os
#get class names for output
dirs = os.listdir('landmark_images/train/')
dirs = list(map(lambda x: x.split('.'),dirs))
dirs = list(map(lambda x : list((int(x[0]),x[1])), dirs))
class_dict = dict(dirs)
class_dict
{0: 'Haleakala_National_Park',
1: 'Mount_Rainier_National_Park',
2: 'Ljubljana_Castle',
3: 'Dead_Sea',
4: 'Wroclaws_Dwarves',
5: 'London_Olympic_Stadium',
6: 'Niagara_Falls',
7: 'Stonehenge',
8: 'Grand_Canyon',
9: 'Golden_Gate_Bridge',
10: 'Edinburgh_Castle',
11: 'Mount_Rushmore_National_Memorial',
12: 'Kantanagar_Temple',
13: 'Yellowstone_National_Park',
14: 'Terminal_Tower',
15: 'Central_Park',
16: 'Eiffel_Tower',
17: 'Changdeokgung',
18: 'Delicate_Arch',
19: 'Vienna_City_Hall',
20: 'Matterhorn',
21: 'Taj_Mahal',
22: 'Moscow_Raceway',
23: 'Externsteine',
24: 'Soreq_Cave',
25: 'Banff_National_Park',
26: 'Pont_du_Gard',
27: 'Seattle_Japanese_Garden',
28: 'Sydney_Harbour_Bridge',
29: 'Petronas_Towers',
30: 'Brooklyn_Bridge',
31: 'Washington_Monument',
32: 'Hanging_Temple',
33: 'Sydney_Opera_House',
34: 'Great_Barrier_Reef',
35: 'Monumento_a_la_Revolucion',
36: 'Badlands_National_Park',
37: 'Atomium',
38: 'Forth_Bridge',
39: 'Gateway_of_India',
40: 'Stockholm_City_Hall',
41: 'Machu_Picchu',
42: 'Death_Valley_National_Park',
43: 'Gullfoss_Falls',
44: 'Trevi_Fountain',
45: 'Temple_of_Heaven',
46: 'Great_Wall_of_China',
47: 'Prague_Astronomical_Clock',
48: 'Whitby_Abbey',
49: 'Temple_of_Olympian_Zeus'}
class_dict = {0: 'Haleakala_National_Park',
1: 'Mount_Rainier_National_Park',
2: 'Ljubljana_Castle',
3: 'Dead_Sea',
4: 'Wroclaws_Dwarves',
5: 'London_Olympic_Stadium',
6: 'Niagara_Falls',
7: 'Stonehenge',
8: 'Grand_Canyon',
9: 'Golden_Gate_Bridge',
10: 'Edinburgh_Castle',
11: 'Mount_Rushmore_National_Memorial',
12: 'Kantanagar_Temple',
13: 'Yellowstone_National_Park',
14: 'Terminal_Tower',
15: 'Central_Park',
16: 'Eiffel_Tower',
17: 'Changdeokgung',
18: 'Delicate_Arch',
19: 'Vienna_City_Hall',
20: 'Matterhorn',
21: 'Taj_Mahal',
22: 'Moscow_Raceway',
23: 'Externsteine',
24: 'Soreq_Cave',
25: 'Banff_National_Park',
26: 'Pont_du_Gard',
27: 'Seattle_Japanese_Garden',
28: 'Sydney_Harbour_Bridge',
29: 'Petronas_Towers',
30: 'Brooklyn_Bridge',
31: 'Washington_Monument',
32: 'Hanging_Temple',
33: 'Sydney_Opera_House',
34: 'Great_Barrier_Reef',
35: 'Monumento_a_la_Revolucion',
36: 'Badlands_National_Park',
37: 'Atomium',
38: 'Forth_Bridge',
39: 'Gateway_of_India',
40: 'Stockholm_City_Hall',
41: 'Machu_Picchu',
42: 'Death_Valley_National_Park',
43: 'Gullfoss_Falls',
44: 'Trevi_Fountain',
45: 'Temple_of_Heaven',
46: 'Great_Wall_of_China',
47: 'Prague_Astronomical_Clock',
48: 'Whitby_Abbey',
49: 'Temple_of_Olympian_Zeus'}
from PIL import Image
import torch
## TODO: return the names of the top k landmarks predicted by the transfer learned CNN
def predict_landmarks(img_path, k):
img_transform = transforms.Compose([
transforms.Resize((480,480)),
transforms.ToTensor(),
transforms.Normalize((0.5,0.5, 0.5), (0.5,0.5, 0.5))
])
img = Image.open(img_path)
img = img_transform(img)
model = get_transfer_model(50)
model.load_state_dict(torch.load('model_transfer.pt'))
if use_cuda:
model = model.cuda()
model.eval()
with torch.no_grad():
if use_cuda:
img = img.cuda()
out = model(img.unsqueeze(0))
# print(out)
topk = torch.topk(out,k,dim=1)
np_indices = list(topk[1].cpu().numpy()[0])
# print(np_indices)
return [class_dict[x] for x in np_indices]
# test on a sample image
predict_landmarks('images/test/09.Golden_Gate_Bridge/190f3bae17c32c37.jpg', 5)
50
['Golden_Gate_Bridge', 'Forth_Bridge', 'Niagara_Falls', 'Stockholm_City_Hall', 'Brooklyn_Bridge']
In the code cell below, implement the function suggest_locations, which accepts a file path to an image as input, and then displays the image and the top 3 most likely landmarks as predicted by predict_landmarks.
Some sample output for suggest_locations is provided below, but feel free to design your own user experience!

def suggest_locations(img_path):
# get landmark predictions
predicted_landmarks = predict_landmarks(img_path, 3)
## TODO: display image and display landmark predictions
import matplotlib.pyplot as plt
img = plt.imread(Path(img_path))
plt.imshow(img)
plt.text(0,img.shape[0] + 100,f'Is this picture of the {", ".join(list(map(lambda x: x.replace("_"," "),predicted_landmarks[:-1])))} or {predicted_landmarks[-1].replace("_"," ")} ?')
plt.show()
# test on a sample image
suggest_locations('images/test/09.Golden_Gate_Bridge/190f3bae17c32c37.jpg')
50
Test your algorithm by running the suggest_locations function on at least four images on your computer. Feel free to use any images you like.
Question 4: Is the output better than you expected :) ? Or worse :( ? Provide at least three possible points of improvement for your algorithm.
Answer: (Three possible points for improvement)
## TODO: Execute the `suggest_locations` function on
## at least 4 images on your computer.
## Feel free to use as many code cells as needed.
import os
root = Path('landmark_images/test')
dirs = [x for x in root.iterdir() if x.is_dir()]
images = []
for i in dirs:
images.extend([x for x in i.iterdir() if x.is_file()])
for i in range(0,len(images),11):
print(images[i])
suggest_locations(images[i])
landmark_images\test\00.Haleakala_National_Park\042517e40d998160.jpg 50
landmark_images\test\00.Haleakala_National_Park\329b1be89952a6ea.jpg 50
landmark_images\test\00.Haleakala_National_Park\79b4dc3771b2d75e.jpg 50
landmark_images\test\01.Mount_Rainier_National_Park\359e5588b6045ee8.jpg 50
landmark_images\test\01.Mount_Rainier_National_Park\761daa5eded76313.jpg 50
landmark_images\test\02.Ljubljana_Castle\1bafa4266447ca3c.jpg 50
landmark_images\test\02.Ljubljana_Castle\6eafeab9c5b50e93.jpg 50
landmark_images\test\03.Dead_Sea\0b5870c7c410cd37.jpg 50
landmark_images\test\03.Dead_Sea\3e61918614c39f96.jpg 50
landmark_images\test\03.Dead_Sea\7e0f9f4f14eb9af1.jpg 50
landmark_images\test\04.Wroclaws_Dwarves\428e988c4b8d9d3e.jpg 50
landmark_images\test\04.Wroclaws_Dwarves\76f6e138ec02d56c.jpg 50
landmark_images\test\05.London_Olympic_Stadium\2b924dbe0974d3ea.jpg 50
landmark_images\test\05.London_Olympic_Stadium\63a2a1d2880a8b9c.jpg 50
landmark_images\test\06.Niagara_Falls\0c2aef04fe14c796.jpg 50
landmark_images\test\06.Niagara_Falls\428a5466eb258107.jpg 50
landmark_images\test\07.Stonehenge\04d1a195c7e6c899.jpg 50
landmark_images\test\07.Stonehenge\4169404dc415eb89.jpg 50
landmark_images\test\07.Stonehenge\7b38a3fa5b58466c.jpg 50
landmark_images\test\08.Grand_Canyon\2f64a93067e8eb13.jpg 50
landmark_images\test\08.Grand_Canyon\6b659b399ccd04ad.jpg 50
landmark_images\test\09.Golden_Gate_Bridge\1bc7a7f05288153b.jpg 50
landmark_images\test\09.Golden_Gate_Bridge\5ed314bab8075930.jpg 50
landmark_images\test\10.Edinburgh_Castle\19eab04e683dfe4a.jpg 50
landmark_images\test\10.Edinburgh_Castle\56fe194601f8ae9c.jpg 50
landmark_images\test\11.Mount_Rushmore_National_Memorial\0855bcdafebf7158.jpg 50
landmark_images\test\11.Mount_Rushmore_National_Memorial\3e533c14e48e0de3.jpg 50
landmark_images\test\11.Mount_Rushmore_National_Memorial\78f9654319180e1b.jpg 50
landmark_images\test\12.Kantanagar_Temple\2c8606adee9504a5.jpg 50
landmark_images\test\12.Kantanagar_Temple\61be51b7ec39e372.jpg 50
landmark_images\test\13.Yellowstone_National_Park\305775082f1b7f02.jpg 50
landmark_images\test\13.Yellowstone_National_Park\51a0bb6d380b6fef.jpg 50
landmark_images\test\14.Terminal_Tower\1a42461138f606c9.jpg 50
landmark_images\test\14.Terminal_Tower\3eee8eab1d93207a.jpg 50
landmark_images\test\14.Terminal_Tower\7dffcaf9f66d3fc2.jpg 50
landmark_images\test\15.Central_Park\3b005ec4bf8e5b85.jpg 50
landmark_images\test\15.Central_Park\6efa9ed216cfaca5.jpg 50
landmark_images\test\16.Eiffel_Tower\26f82dab964ef649.jpg 50
landmark_images\test\16.Eiffel_Tower\58d099b15ee74b73.jpg 50
landmark_images\test\17.Changdeokgung\0b95751e4ccbbd19.jpg 50
landmark_images\test\17.Changdeokgung\5856e204147c49fa.jpg 50
landmark_images\test\18.Delicate_Arch\0a644b21cc4f7eb5.jpg 50
landmark_images\test\18.Delicate_Arch\2c630f04fcd24719.jpg 50
landmark_images\test\18.Delicate_Arch\774e3f8f7a9c3604.jpg 50
landmark_images\test\19.Vienna_City_Hall\33fdae363340e364.jpg 50
landmark_images\test\19.Vienna_City_Hall\56718759419f44c9.jpg 50
landmark_images\test\20.Matterhorn\1876d4590a15fc46.jpg 50
landmark_images\test\20.Matterhorn\4fba1da3235d07d6.jpg 50
landmark_images\test\21.Taj_Mahal\14a11ddef5fb84af.jpg 50
landmark_images\test\21.Taj_Mahal\5469792e0b15c65f.jpg 50
landmark_images\test\22.Moscow_Raceway\01969b4e5e396b31.jpg 50
landmark_images\test\22.Moscow_Raceway\44f1ca5a50e7ce91.jpg 50
landmark_images\test\22.Moscow_Raceway\6a87c4ef304dc4b3.jpg 50
landmark_images\test\23.Externsteine\2ddb9fd370c9eb08.jpg 50
landmark_images\test\23.Externsteine\664f1b750e82782f.jpg 50
landmark_images\test\24.Soreq_Cave\2f6b1b995778b892.jpg 50
landmark_images\test\24.Soreq_Cave\581cfaa4280a860d.jpg 50
landmark_images\test\25.Banff_National_Park\182da82f82e6a787.jpg 50
landmark_images\test\25.Banff_National_Park\4095e61a71b4e958.jpg 50
landmark_images\test\25.Banff_National_Park\7f1242fc19c69e0a.jpg 50
landmark_images\test\26.Pont_du_Gard\460f9442170554e6.jpg 50
landmark_images\test\26.Pont_du_Gard\6d33150d95313039.jpg 50
landmark_images\test\27.Seattle_Japanese_Garden\24621d0983313599.jpg 50
landmark_images\test\27.Seattle_Japanese_Garden\682139f4cc6412be.jpg 50
landmark_images\test\28.Sydney_Harbour_Bridge\16c0f11519d5a2b5.jpg 50
landmark_images\test\28.Sydney_Harbour_Bridge\4e25a44e02916049.jpg 50
landmark_images\test\29.Petronas_Towers\0ae950a45582e3a1.jpg 50
landmark_images\test\29.Petronas_Towers\3dfb07dbf222ed0a.jpg 50
landmark_images\test\29.Petronas_Towers\7a60edac1920a106.jpg 50
landmark_images\test\30.Brooklyn_Bridge\2b2061fbe0abcb7c.jpg 50
landmark_images\test\30.Brooklyn_Bridge\76b3106b046d3f45.jpg 50
landmark_images\test\31.Washington_Monument\1d18a23956807778.jpg 50
landmark_images\test\31.Washington_Monument\3f92ba97b309ced8.jpg 50
landmark_images\test\32.Hanging_Temple\1ce97b98d1313aaf.jpg 50
landmark_images\test\32.Hanging_Temple\6b4607c002f7f621.jpg 50
landmark_images\test\33.Sydney_Opera_House\08087d98660bdbd8.jpg 50
landmark_images\test\33.Sydney_Opera_House\2c89aedd9e0ee12a.jpg 50
landmark_images\test\33.Sydney_Opera_House\72be97459cedc17c.jpg 50
landmark_images\test\34.Great_Barrier_Reef\4a0ea6d00fffc5de.jpg 50
landmark_images\test\34.Great_Barrier_Reef\685ce33c2c8b0178.jpg 50
landmark_images\test\35.Monumento_a_la_Revolucion\1c793b20cdbb8b11.jpg 50
landmark_images\test\35.Monumento_a_la_Revolucion\57ae5d0f31394c5a.jpg 50
landmark_images\test\36.Badlands_National_Park\0e3c975ccbf0442a.jpg 50
landmark_images\test\36.Badlands_National_Park\4dde9187cb31166e.jpg 50
landmark_images\test\36.Badlands_National_Park\7bd45178170d75c6.jpg 50
landmark_images\test\37.Atomium\2ac5b8d85225d0f9.jpg 50
landmark_images\test\37.Atomium\7657e4e93df87dca.jpg 50
landmark_images\test\38.Forth_Bridge\3747ee03a240fa8b.jpg 50
landmark_images\test\38.Forth_Bridge\6183bc69574d7045.jpg 50
landmark_images\test\39.Gateway_of_India\1e7955ba85230d5c.jpg 50
landmark_images\test\39.Gateway_of_India\58cc79f7c256fe4f.jpg 50
landmark_images\test\40.Stockholm_City_Hall\13b55db86563dac5.jpg 50
landmark_images\test\40.Stockholm_City_Hall\446cfcfa4d1d852d.jpg 50
landmark_images\test\40.Stockholm_City_Hall\7ded0527ac0c860b.jpg 50
landmark_images\test\41.Machu_Picchu\2f0f11e947907961.jpg 50
landmark_images\test\41.Machu_Picchu\529aeaecb9ba4257.jpg 50
landmark_images\test\42.Death_Valley_National_Park\1a8bf9cb96818d8d.jpg 50
landmark_images\test\42.Death_Valley_National_Park\5a4dca33b5a8b74f.jpg 50
landmark_images\test\43.Gullfoss_Falls\1ebc2ece28e1cbad.jpg 50
landmark_images\test\43.Gullfoss_Falls\5c1d0b53fce01610.jpg 50
landmark_images\test\44.Trevi_Fountain\097ff54ab4271a6f.jpg 50
landmark_images\test\44.Trevi_Fountain\41c52ea1d3b7593a.jpg 50
landmark_images\test\44.Trevi_Fountain\791981db7256a47d.jpg 50
landmark_images\test\45.Temple_of_Heaven\2af4218fe2a1f900.jpg 50
landmark_images\test\45.Temple_of_Heaven\6356845282dfcde3.jpg 50
landmark_images\test\46.Great_Wall_of_China\25e08232feee7ea3.jpg 50
landmark_images\test\46.Great_Wall_of_China\4a7209de41431e9b.jpg 50
landmark_images\test\47.Prague_Astronomical_Clock\0fcdf206e1e36f05.jpg 50
landmark_images\test\47.Prague_Astronomical_Clock\59f46ae80330519f.jpg 50
landmark_images\test\47.Prague_Astronomical_Clock\7d807d38b49395bb.jpg 50
landmark_images\test\48.Whitby_Abbey\376e025c0da1b4b6.jpg 50
landmark_images\test\48.Whitby_Abbey\75dc03b37fea0ae4.jpg 50
landmark_images\test\49.Temple_of_Olympian_Zeus\34aa0fbd65529602.jpg 50
landmark_images\test\49.Temple_of_Olympian_Zeus\5eaecfb5d906417b.jpg 50